The present invention relates generally to semiconductor processing, and in particular to a system and method for determining shapes of features having variable contrast resulting from a semiconductor manufacturing process.
In the semiconductor industry, there is a continuing trend toward higher device densities. To achieve these high densities there has been and continues to be efforts toward scaling down the device dimensions (e.g., at submicron levels) on semiconductor wafers. In order to accomplish such high device packing density, smaller and smaller features sizes are required. This may include the width and spacing of interconnecting lines, spacing and diameter of contact holes, and the surface geometry such as corners and edges of various features.
The requirement of small features with close spacing between adjacent features requires high resolution photolithographic processes. In general, lithography refers to processes for pattern transfer between various media. It is a technique used for integrated circuit fabrication in which a silicon slice, the wafer, is coated uniformly with a radiation-sensitive film, the resist, and an exposing source (such as optical light, x-rays, etc.) illuminates selected areas of the surface through an intervening master template, the mask, for a particular pattern. The lithographic coating is generally a radiation-sensitive coating suitable for receiving a projected image of the subject pattern. Once the image is projected, it is indelibly formed in the coating. The projected image may be either a negative or a positive image of the subject pattern. Exposure of the coating through a photomask causes the image area to become either more or less soluble (depending on the coating) in a particular solvent developer. The more soluble areas are removed in the developing process to leave the pattern image in the coating as less soluble polymer.
Due to the extremely fine patterns which are exposed on the photoresist, Scanning Electron Microscopes (SEMS) may be employed to analyze and measure critical dimensions resulting from the lithographic process. Critical dimensions may include the size of minimum features across the wafer such as linewidth, spacing, and contact dimensions, for example. Analytical portions within the SEMs may then utilize pattern recognition algorithms to determine the feature""s shape wherein scanned features may be compared to stored predetermined patterns. Unfortunately, conventional pattern recognition systems often fail to correctly identify scanned features.
One such problem associated with a conventional pattern recognition method relates to correctly identifying features that may have varying contrast levels even though a pattern may be the same from one brightness level to the next. For example, pattern features, such as contact holes, may have opposite contrast levels depending on the actual wafer layer currently being scanned. Conventional pattern recognition systems often employ pixel comparison algorithms to identify such features, for example. This may involve storing a set of pixels in memory for each of a plurality of desired or expected feature profiles/templates. Under ideal circumstances, as actual semiconductor features are scanned by the SEM system, a memory pixel comparison is performed between the scanned feature and the stored feature. If enough pixels match between one of the stored profiles and the scanned feature profile, an identification of the feature is then determined based upon the match.
If, however, the feature being analyzed is scanned over multiple layers for example, pixel contrast levels/values will likely change for the scanned feature depending on the layer scanned. As features are scanned over multiple layers, it is noted that different layers can correspond to different substrates and materials. Thus, contrast levels/values can change. Unfortunately, the stored feature profiles described above are often stored with an absolute pixel value corresponding to a static and/or fixed value for the feature. When a similar feature is scanned having differing pixel contrast values than the stored profiles, a mismatch is likely to occur between the stored profile and the scanned feature based upon conventional pixel comparison algorithms. This may result in an incorrect identification of the feature since none of the stored profiles may match the scanned feature profile. Thus, an acceptable feature may be incorrectly rejected merely by having differing pixel contrast values than the stored feature profile. Consequently, there is a need for a system and/or method which improves feature pattern recognition across varying contrast levels.
The present invention relates to a system and method for improving feature pattern recognition within SEM systems. A plurality of signal scans may be provided by an SEM system during critical dimension measurements which may then be utilized by the SEM and/or other computer systems to determine the feature""s shape. In contrast to conventional pixel comparison systems wherein features are determined by matching scanned pixels with stored absolute value pixel profiles, the present invention employs a signal analysis despite varying contrast levels of the scanned feature to identify the shape. This may be achieved by determining a first set of signal contrast regions of a stored feature profile and comparing those regions to second set of signal contrast regions from the scanned feature. If the compared contrast regions have substantially constant brightness at the defined regions, it may be determined that the features are the same. In this manner, features may be determined across multiple layers of a structure even though contrast levels differ amongst the layers. Thus, feature misidentifications associated with conventional systems are mitigated.
More particularly, the present invention employs an SEM scan analysis and comparison algorithm to perform pattern recognition of features having variable contrast. Pattern recognition is achieved by performing an alignment and a contrast analysis between a signal associated with a stored feature and a signal received from the SEM system. The SEM signal may be provided, for example, by performing an SEM scan across the feature. Alignment of the scanned signal and the stored signal may then be achieved by positioning edges of each signal in a defined relationship with each other. After signal alignment, the contrast analysis is performed on a set of regions defined by varying contrast levels of the scanned signals. As regions of the signals are analyzed, a determination is made as to whether the brightness level is substantially constant (e.g., within a predetermined threshold of variation) across the regions. If the scanned contrast regions having substantially constant brightness correspond to the stored profile regions having substantially constant brightness, the feature""s shape may be determined despite the varying contrast levels.
To the accomplishment of the foregoing and related ends, the invention comprises the features hereinafter fully described. The following description and the annexed drawings set forth in detail certain illustrative embodiments of the invention. These embodiments are indicative, however, of but a few of the various ways in which the principles of the invention may be employed. Other objects, advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the drawings.